Key Takeaways
- MIT license β the core Python framework is free, permissively licensed, and actively maintained
- Core building blocks: Documents/Nodes feed an Index (commonly a VectorStoreIndex), a Retriever pulls relevant nodes, a Query Engine answers the question
- Free to install and self-host: pip install llama-index, bring your own LLM provider and vector store
- LlamaCloud is a separate, paid, usage-based platform for automated document parsing (LlamaParse) and extraction β not required to use the open-source framework
- Code-first: there is no visual canvas β this is a Python library you import and write against
- The TypeScript SDK, LlamaIndex.TS, was archived and marked deprecated on April 30, 2026 β Python is the actively maintained implementation
- Best fit: developers building production RAG systems who want programmatic control over indexing and retrieval logic
π In One Sentence
LlamaIndex is a free, open-source (MIT) Python framework for connecting large language models to your own data through indexing, retrieval, and query engines β built for developers who write code, not for visual no-code builders.
π¬ In Plain Terms
Instead of dragging boxes on a canvas, LlamaIndex gives you Python classes: load your documents, build an index, then query it β every step is code you can version, test, and customize.
πNote: If you want to build a RAG pipeline by connecting boxes instead of writing Python, LlamaIndex is the wrong tool β look at a visual builder like Flowise, Langflow, or Dify instead.
What Is LlamaIndex?
LlamaIndex (github.com/run-llama/llama_index) is an open-source, MIT-licensed Python framework for building applications that connect large language models to your own data. It is a data framework first: the library is organized around ingesting documents, structuring them for retrieval, and answering questions over them β not around general-purpose agent orchestration.
- Data connectors: SimpleDirectoryReader for local files, plus 300+ community and first-party integrations on LlamaHub for PDFs, APIs, SQL databases, and other sources
- Indices: structures that organize ingested data for LLM retrieval β VectorStoreIndex (embeddings-based semantic search) is the most common
- Retrievers: fetch the nodes relevant to a query from an index
- Query engines: combine a retriever with an LLM call to answer a question over your data β the core retrieval-augmented generation (RAG) loop
- Workflows: an event-driven system for orchestrating multi-step agents and pipelines beyond a single query
- The core Python repository reports over 50,000 GitHub stars
What Are LlamaIndex's Core Building Blocks?
LlamaIndex organizes a RAG pipeline into five building blocks: Document/Node, Index, Retriever, Query Engine, and Workflow. Each maps to one step in turning raw files into an LLM that can answer questions about them.
Document / Node
- What it does:
- Raw data, then chunked structured pieces of it
Index
- What it does:
- Organizes nodes for retrieval (e.g. VectorStoreIndex)
Retriever
- What it does:
- Fetches the nodes relevant to a query
Query Engine
- What it does:
- Combines retrieval + an LLM call to answer a question
Workflow
- What it does:
- Event-driven orchestration for multi-step agents
How Does LlamaIndex Differ From LangChain and Visual Builders?
LlamaIndex is a data framework first, LangChain is a general-purpose orchestration framework, and tools like Flowise, Langflow, and Dify are visual, no-code builders β all three solve different problems even when they overlap on RAG. Picking between them is a question of what you are optimizing for, not which one is "better."
Is LlamaIndex Free, or Do You Need to Pay for LlamaCloud?
The core LlamaIndex framework is free forever under the MIT license β you can self-host it against your own LLM and vector database at no cost to LlamaIndex. LlamaCloud, a separate hosted platform for automated document parsing and extraction (LlamaParse, LlamaExtract), charges usage-based credits on top of that: $1.25 per 1,000 credits, with parsing costing 1β45 credits per page and extraction 5β60 credits per page depending on the tier selected.
- Open-source framework (pip install llama-index): free, MIT-licensed, self-hosted β no account or credits required
- LlamaCloud / LlamaParse: paid, credit-metered document parsing for layout-heavy PDFs, scanned documents, tables, and charts
- You do not need a LlamaCloud account to build or run a RAG pipeline with the open-source framework β SimpleDirectoryReader and other free loaders cover plain text, Markdown, and standard PDFs
- LlamaCloud becomes relevant when your documents are complex enough (multi-column layouts, embedded tables, handwriting) that a free parser produces poor chunk quality
How Do You Build a Minimal RAG Pipeline With LlamaIndex?
A working LlamaIndex RAG pipeline is five lines of Python: load documents, build an index, create a query engine, and query it. This example uses the default in-memory vector store and an OpenAI-compatible LLM; swap in a local provider (Ollama, for example) by installing its integration package and setting Settings.llm before building the index.
- 1Install the framework: pip install llama-index (add a provider package such as llama-index-llms-ollama to use a local model instead of a hosted API).
- 2Set your LLM provider credentials β for example export OPENAI_API_KEY=... β or configure Settings.llm to point at a local provider before the next step.
- 3Place your source files in a folder (for example data/) and load them: documents = SimpleDirectoryReader("data").load_data().
- 4Build an index from the loaded documents: index = VectorStoreIndex.from_documents(documents). This chunks each document into Nodes and embeds them.
- 5Create a query engine from the index: query_engine = index.as_query_engine().
- 6Query your data and print the answer: response = query_engine.query("Your question here"); print(response).
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
# Load documents from a folder
documents = SimpleDirectoryReader("data").load_data()
# Build an index from documents
index = VectorStoreIndex.from_documents(documents)
# Create a query engine
query_engine = index.as_query_engine()
# Query your data
response = query_engine.query("Your question here")
print(response)Does this index rebuild every time the script runs?
Yes, by default. For anything beyond a one-off script, call index.storage_context.persist() after building the index, and load it back with load_index_from_storage() on subsequent runs instead of re-embedding every document each time.
Can I use a local LLM and vector store instead of OpenAI?
Yes. Install the matching integration package (for example llama-index-llms-ollama for a local model, or a vector-store package such as llama-index-vector-stores-chroma), then set Settings.llm and Settings.embed_model, or pass the vector store into VectorStoreIndex.from_documents(), before running the same query engine code.
Is the TypeScript Version of LlamaIndex Still Maintained?
No. LlamaIndex.TS (run-llama/LlamaIndexTS on GitHub) was archived by its maintainers and marked deprecated on April 30, 2026. The repository's own notice states the project "is deprecated and no longer maintained" and directs users toward the LlamaCloud/LlamaParse documentation instead of the TypeScript SDK.
- The Python framework (llama_index) is the actively maintained implementation β this article covers that version
- Existing TypeScript projects built on LlamaIndex.TS will keep running, but receive no further updates, bug fixes, or security patches
- For a new TypeScript or JavaScript project, evaluate LangChain.js (actively maintained, MIT-licensed) instead of starting on an archived library
Who Should Use LlamaIndex?
LlamaIndex fits a specific position in the RAG tooling landscape: developers who want code-level control over indexing and retrieval, not the fastest path to a visual prototype.
LlamaIndex vs. Alternatives
The right choice depends on whether you want code-level control (LlamaIndex, LangChain, Haystack) or a visual canvas (Flowise and similar tools).
| Tool | Interface | License | Best For | Maintenance |
|---|---|---|---|---|
| LlamaIndex | Python code | MIT | Data-heavy custom RAG pipelines | Active |
| LangChain | Python / JS code | MIT | General-purpose LLM orchestration | Active |
| Haystack | Python code | Apache 2.0 | Enterprise search + RAG pipelines | Active |
| Flowise | Visual, drag-and-drop | Apache 2.0 | No-code prototyping (unmaintained) | Archived / none |
Common Mistakes When Evaluating LlamaIndex
These mistakes come from treating LlamaIndex like a different category of tool than what it actually is.
Frequently Asked Questions
Is LlamaIndex free to use?
Yes. The core Python framework is free and open-source under the MIT license. LlamaCloud, a separate hosted platform for document parsing and extraction, charges usage-based credits ($1.25 per 1,000 credits), but it is optional and not required to build or run a RAG pipeline.
What license is LlamaIndex released under?
MIT License, for both the Python framework (llama_index) and, while it was maintained, the TypeScript SDK (LlamaIndex.TS). MIT permits commercial use, modification, and redistribution.
How is LlamaIndex different from LangChain?
LlamaIndex is a data framework built specifically around indexing and retrieval β Documents, Nodes, Indices, Retrievers, and Query Engines. LangChain is a general-purpose orchestration framework covering chains, agents, and tool-calling, where retrieval is one capability among many. Both are MIT-licensed, Python-first, and actively maintained; many production stacks use both together.
Is LlamaIndex a visual, no-code builder?
No. LlamaIndex is a Python (previously also TypeScript) library you import and write code against β there is no drag-and-drop canvas. For a visual, no-code approach to RAG or agent pipelines, evaluate Flowise, Langflow, or Dify instead.
What is LlamaCloud, and do I need it?
LlamaCloud is a separate, paid, usage-based platform from the same company, offering LlamaParse (document parsing) and LlamaExtract (structured extraction) for complex documents. You do not need it to use the open-source LlamaIndex framework β it only becomes useful when free document loaders produce poor results on complex PDFs, scanned pages, or dense tables.
Is the TypeScript version of LlamaIndex still maintained?
No. LlamaIndex.TS was archived by its maintainers and marked deprecated on April 30, 2026. The Python framework remains the actively maintained implementation. For a new TypeScript/JavaScript project, evaluate LangChain.js instead.
What is a Query Engine in LlamaIndex?
A Query Engine combines a Retriever with an LLM call into one method call. Calling index.as_query_engine().query("your question") retrieves the relevant Nodes from the index, builds a prompt containing them, and returns the model's answer β the core retrieval-augmented generation (RAG) loop.
How many integrations does LlamaIndex support?
LlamaHub hosts more than 300 community and first-party integration packages covering data loaders (PDFs, APIs, SQL databases, and more), LLM providers, and vector stores, on top of the built-in SimpleDirectoryReader for local files.
Can I use a local LLM with LlamaIndex instead of a cloud API?
Yes. Install the matching provider integration (for example llama-index-llms-ollama for Ollama), then set it as the default via Settings.llm before building your index β the rest of the Document/Index/Retriever/Query Engine pipeline works the same regardless of which LLM provider you use.
Do I need to write Python to use LlamaIndex?
Yes, for the currently maintained version. LlamaIndex is a code-first framework with no visual interface. Non-developers or teams that want a no-code approach should evaluate a visual builder like Flowise, Langflow, or Dify instead.